Chemical ammonia addition cascade control method based on improved particle swarm optimization feedforward model

Abstract

The present invention discloses a chemical ammonia addition cascade control method based on an improved particle swarm optimization feedforward model, which comprises the following steps: establishing an improved particle swarm optimization feedforward model; inputting the feed water flow rate, the conductivity of the ammonia solution, and the set value of the pH value at the inlet of the economizer into the improved particle swarm optimization feedforward model to generate the first predicted frequency of the ammonia addition pump; inputting the difference between the actual pH value at the inlet of the economizer and the set value of the pH value at the inlet of the economizer into an outer-loop PI controller to generate the set value of the pH value at the inlet of the deaerator; inputting the difference between the actual pH value at the inlet of the deaerator and the set value of the pH value at the inlet of the deaerator into an inner-loop PI controller to generate the second predicted frequency of the ammonia addition pump; adding the first predicted frequency of the ammonia addition pump and the second predicted frequency of the ammonia addition pump to obtain the total ammonia addition pump frequency, so as to realize the automatic control of chemical ammonia addition; by designing the structure of the feedforward model and using the improved particle swarm algorithm to identify the parameters of the feedforward model, the present invention can accurately predict the ammonia addition demand.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical automatic control, and specifically to a chemical ammonia addition cascade control method based on an improved particle swarm optimization feedforward model. Background Art

[0002] In the feed water ammonia addition system of coal-fired units, ammonia water is added at the boiler feed water pipeline to keep the boiler feed water weakly alkaline and prevent the boiler feed water from corroding metals. Currently, most units adjust the frequency of the ammonia addition pump manually to change the ammonia addition amount and control the pH value of the feed water. However, due to the large time-delay characteristics of the chemical on-line instrument measurement and sampling link, the change of the pH value of the feed water lags behind the flow rate change under variable load conditions, resulting in the inability of the operating personnel to add ammonia in a timely and accurate manner, causing large fluctuations in the pH value of the feed water and posing a risk of metal corrosion, which affects the safe operation of the unit. Summary of the Invention

[0003] Aiming at the deficiencies of the prior art, the present invention provides a chemical ammonia addition cascade control method based on an improved particle swarm optimization feedforward model, aiming to suppress load interference, maintain the stability of the pH value of the feed water, and ensure the safe operation of the unit.

[0004] To achieve the above object, the present invention provides the following technical solution: A chemical ammonia addition cascade control method based on an improved particle swarm optimization feedforward model, including the following steps:

[0005] Step S1: Establish an improved particle swarm optimization feedforward model;

[0006] By screening the relevant variables related to the frequency of the ammonia addition pump during the operation of the thermal power unit and considering the time-delay relationship between the mechanism of the thermal power unit and each relevant variable, a feedforward model is constructed, and the parameters of the feedforward model are identified by using an improved particle swarm algorithm to obtain an improved particle swarm optimization feedforward model;

[0007] The specific process of using the improved particle swarm algorithm to identify the parameters of the feedforward model is as follows:

[0008] Step S1.21: Based on the parameters of the feedforward model , , , , , set the position of the th particle individual of the improved particle swarm algorithm to be , and the fitness function of the improved particle swarm algorithm is the variance of the prediction error of the ammonia addition pump frequency; is the lag time of the feed water flow rate, is the lag time of the conductivity of the ammonia solution, is the lag time of the pH value at the inlet of the economizer, and are the weight and bias of the pH value at the economizer inlet respectively, represents time;

[0009] Step S1.22: Initialize the improved particle swarm optimization algorithm; determine the number of particle individuals, the initial positions and velocities of each particle individual;

[0010] Step S1.23: Calculate the fitness of the position of each particle individual according to the fitness function;

[0011] Step S1.24: Update the extreme value of the particle individual position, that is, the historical optimal position of the particle individual: if the fitness of the current particle individual position is better than the global extreme value, that is, the optimal position of the current particle individual, then update the extreme value of the particle individual position ;

[0012] Step S1.25: Update the global extreme value: if the fitness of the current particle individual position is better than the global extreme value, then update the global extreme value ;

[0013] Step S1.26: Update the particle individual velocity: update the velocity of each particle individual according to the extreme value of the particle individual position and the global extreme value;

[0014] Step S1.27: Update the particle individual position according to the updated particle individual velocity;

[0015] Step S1.28: Repeat steps S1.23 - S1.27 until the preset stop condition, that is, the maximum number of iterations, is satisfied;

[0016] Step S1.29: After the preset stop condition is satisfied, output the particle individual position corresponding to the global extreme value as the optimal solution, that is, the optimal feedforward model parameters;

[0017] Step S2: Input the feedwater flow rate, the conductivity of the ammonia solution, and the set value of the pH at the economizer inlet into the improved particle swarm optimization feedforward model to generate the first predicted frequency of the ammonia addition pump;

[0018] Step S3: Input the difference between the actual pH value at the economizer inlet and the set value of the pH at the economizer inlet into the outer - loop PI controller to generate the set value of the pH at the deaerator inlet, that is, the set value of the inner - loop PI controller;

[0019] Step S4: Input the difference between the actual pH value at the deaerator inlet and the set value of the pH at the deaerator inlet into the inner - loop PI controller to generate the second predicted frequency of the ammonia addition pump;

[0020] Step S5: Add the first predicted frequency of the ammonia addition pump and the second predicted frequency of the ammonia addition pump to obtain the total frequency of the ammonia addition pump, realizing automatic chemical ammonia addition control.

[0021] Furthermore, the feedforward model is expressed as:

[0022] ;

[0023] In the formula, is the frequency of the ammonia addition pump at time is the feed water flow rate at time is the set value of the pH at the inlet of the economizer at time is the conductivity of the ammonia solution at time

[0024] Furthermore, in step S1.26, the update formula for the particle individual velocity is:

[0025] ;

[0026] ;

[0027] In the formula, represents the inertia weight; and represent the maximum and minimum values of the inertia weight respectively; represents the base of the natural logarithm; represents the current iteration number; represents the maximum iteration number; represents the adjustment factor, taking 0.1; represents the beta distribution; both represent the shape parameters of the beta distribution; represents the velocity of the th particle individual at time ; is the velocity of the th particle individual at time ; and are the learning factors; and are random numbers in the range [0, 1]; represents the extreme value of the particle individual position at time represents the global extreme value at time

[0028] Furthermore, in step S1.27, the particle individual position is updated according to the updated particle individual velocity, expressed as:

[0029] ;

[0030] ;

[0031] In the formula, represents the position of the th particle individual at moment; represents the dynamic adjustment factor.

[0032] Furthermore, the specific process of step S3 is as follows: The difference between the actual value of the pH at the economizer inlet and the set value of the pH at the economizer inlet is input into the outer-loop PI controller, and the set value of the pH at the deaerator inlet is calculated through the proportional and integral controllers in the outer-loop PI controller , that is, the set value of the inner-loop PI controller, which is expressed as:

[0033] ;

[0034] In the formula, is the proportional gain coefficient of the outer-loop PI controller; is the integral gain coefficient of the outer-loop PI controller; is the difference between the set value of the pH at the economizer inlet and the actual value of the pH at the economizer inlet at moment; is

[0035] the difference between the set value of the pH at the economizer inlet and the actual value of the pH at the economizer inlet at Furthermore, the specific process of step S4 is as follows: The difference between the actual value of the pH at the deaerator inlet and the set value of the pH at the deaerator inlet is input into the inner-loop PI controller, and the frequency of the second ammonia addition pump is calculated through the proportional and integral controllers in the inner-loop PI controller

[0036] ;

[0037] In the formula, is the proportional gain coefficient of the inner-loop PI controller; is the integral gain coefficient of the inner-loop PI controller; represents the difference between the set value of the pH at the deaerator inlet and the actual value of the pH at the deaerator inlet at moment; is

[0038] Furthermore, when the outer-loop PI controller calculates the set value of the pH at the deaerator inlet and the inner-loop PI controller calculates the frequency of the second ammonia addition pump , a first-order inertial link is added for filtering.

[0039] An electronic device includes a processor, a memory, and a bus. The processor and the memory are connected through the bus. Among them, the memory is used to store a set of program codes, and the processor is used to call the program codes stored in the memory and execute a chemical ammonia addition cascade control method based on an improved particle swarm optimization feedforward model.

[0040] A non-volatile computer storage medium stores computer-executable instructions, and these computer-executable instructions execute a chemical ammonia addition cascade control method based on an improved particle swarm optimization feedforward model.

[0041] Compared with the existing technologies, the present invention has the following beneficial effects:

[0042] (1) By adopting the scheme of feedforward plus cascade PID algorithm, the present invention realizes the automatic control of ammonia addition to feed water, greatly reduces the manual workload, and saves labor costs.

[0043] (2) By analyzing the system mechanism and the time-delay relationship of each variable, the present invention determines the feedforward model structure, and uses the improved particle swarm algorithm to identify the feedforward model parameters, which can realize the accurate prediction of ammonia addition demand and can add ammonia accurately and in a timely manner.

[0044] (3) By adopting the nonlinear attenuation method and the beta distribution function to improve the inertia weight update formula, and by using the current individual extreme value and the global extreme value to construct a dynamic adjustment factor to adaptively adjust the step size and improve the position update formula, the present invention improves the global search ability of the particle swarm algorithm.

[0045] (4) Based on the cascade PID algorithm, the present invention uses the pH value at the inlet of the deaerator as the inner-loop control variable and the pH value at the inlet of the economizer as the outer-loop control variable, overcomes the nonlinear and large time-delay characteristics of the ammonia addition to feed water system, can quickly respond to load changes, effectively suppress load disturbances, maintain the stability of the boiler feed water pH value, and ensure the safe and economic operation of the unit. Description of the Drawings

[0046] Figure 1 It is the method flow chart of the present invention.

[0047] Figure 2 It is the principle diagram of the method of the present invention. Detailed Embodiments

[0048] As Figure 1 - Figure 2 shown, the present invention provides a technical solution: a chemical ammonia addition cascade control method based on an improved particle swarm optimization feedforward model, including the following steps:

[0049] Step S1: Establish an improved particle swarm optimization feedforward model.

[0050] Step S2: Input the feed water flow rate, the conductivity of the ammonia solution, and the set value of the pH at the economizer inlet into the improved particle swarm optimization feedforward model to generate the first predicted frequency of the ammonia addition pump; the set value of the pH at the economizer inlet is usually a fixed value of 9.2.

[0051] Step S3: Input the difference between the actual value of the pH at the economizer inlet and the set value of the pH at the economizer inlet into the outer loop PI controller to generate the set value of the pH at the deaerator inlet, that is, the set value of the inner loop PI controller.

[0052] Step S4: Input the difference between the actual value of the pH at the deaerator inlet and the set value of the pH at the deaerator inlet into the inner loop PI controller to generate the second predicted frequency of the ammonia addition pump.

[0053] Step S5: Add the first predicted frequency of the ammonia addition pump and the second predicted frequency of the ammonia addition pump to obtain the total frequency of the ammonia addition pump, realizing automatic chemical ammonia addition control.

[0054] Among them, the specific process of step S1 is as follows: By screening the relevant variables of the thermal power unit during operation related to the frequency of the ammonia addition pump, and considering the time-delay relationship between the mechanism of the thermal power unit and each relevant variable, a feedforward model is constructed, and the improved particle swarm algorithm is used to identify the parameters of the feedforward model to obtain the improved particle swarm optimization feedforward model.

[0055] Among them, the feedforward model is expressed as:

[0056] ;

[0057] In the formula, is the frequency of the ammonia addition pump at time , , , , , are the parameters of the feedforward model, which are the lag time of the feed water flow rate, the lag time of the conductivity of the ammonia solution, the lag time of the pH value at the economizer inlet, the weight and the bias of the pH value at the economizer inlet; is the feed water flow rate at time ; is the set value of the pH at the economizer inlet at time ; is the conductivity of the ammonia solution at time

[0058] Among them, the specific process of using the improved particle swarm algorithm to identify the parameters of the feedforward model is as follows:

[0059] Step S1.21: Based on the parameters of the feedforward model , , , , , set the position of the th particle individual of the improved particle swarm algorithm as , and the fitness function of the improved particle swarm algorithm is the variance of the prediction error of the ammonia addition pump frequency .

[0060] Step S1.22: Initialize the improved particle swarm algorithm; determine the number of particle individuals, the initial position and velocity of each particle individual.

[0061] Step S1.23: Calculate the fitness of each particle individual position according to the fitness function.

[0062] Step S1.24: Update the particle individual position extremum (the historical optimal position of the particle individual): If the fitness of the current particle individual position is better than the global extremum (the optimal position of the current particle individual), then update the particle individual position extremum .

[0063] Step S1.25: Update the global extremum: If the fitness of the current particle individual position is better than the global extremum, then update the global extremum .

[0064] Step S1.26: Update the particle individual velocity: Update the velocity of each particle individual according to the particle individual position extremum and the global extremum. The update formula of the particle individual velocity is:

[0065] ;

[0066] ;

[0067] In the formula, represents the inertia weight; and represent the maximum and minimum values of the inertia weight respectively; represents the base of the natural logarithm; represents the current iteration number; represents the maximum iteration number; represents the adjustment factor, taking 0.1; represents the beta distribution; both represent the shape parameters of the beta distribution. In this embodiment, takes 1, takes 3; represents the th particle individual at time; is the th particle individual at The velocity at a moment; and is the learning factor; and is a random number in the range of [0, 1]; represents the extreme value of the particle individual position at the moment; represents the global extreme value at the moment.

[0068] Step S1.27: Update the particle individual position according to the updated particle individual velocity, expressed as:

[0069] ;

[0070] ;

[0071] In the formula, represents the th particle individual's position at the moment; represents the dynamic adjustment factor.

[0072] In the present invention, by adopting the non - linear attenuation method and the beta distribution function to improve the inertia weight update formula, and by constructing the dynamic adjustment factor with the current individual extreme value and the global extreme value to adaptively adjust the step size and improve the position update formula, the global search ability of the particle swarm algorithm is improved.

[0073] Step S1.28: Repeat the iterative steps S1.23 - step S1.27 until the preset stop condition, i.e., the maximum number of iterations, is satisfied.

[0074] Step S1.29: After the preset stop condition is satisfied, output the particle individual position corresponding to the global extreme value as the optimal solution, i.e., the optimal feed - forward model parameters.

[0075] In this embodiment, the optimal solution of the improved particle swarm optimization feed - forward model parameters is shown in Table 1 and can be adjusted according to specific situations.

[0076] Table 1 Optimal solution of the improved particle swarm optimization feed - forward model parameters

[0077]

[0078] Among them, the specific process of step S3 is as follows: Input the difference between the actual value of the economizer inlet pH and the set value of the economizer inlet pH into the outer - loop PI controller, and calculate the set value of the deaerator inlet pH through the proportional and integral controllers in the outer - loop PI controller , that is, the set value of the inner - loop PI controller, expressed as:

[0079] ;

[0080] In the formula, is the proportional gain coefficient of the outer-loop PI controller; is the integral gain coefficient of the outer-loop PI controller; is the difference between the set value of the pH at the economizer inlet and the actual value of the pH at the economizer inlet at time is the difference between the set value of the pH at the economizer inlet and the actual value of the pH at the economizer inlet at time

[0081] Among them, the specific process of step S4 is: input the difference between the actual value of the pH at the deaerator inlet and the set value of the pH at the deaerator inlet into the inner-loop PI controller, and calculate the frequency of the second ammonia addition pump through the proportional and integral controllers in the inner-loop PI controller , expressed as:

[0082] ;

[0083] In the formula, is the proportional gain coefficient of the inner-loop PI controller; is the integral gain coefficient of the inner-loop PI controller; represents the difference between the set value of the pH at the deaerator inlet and the actual value of the pH at the deaerator inlet at time is the difference between the set value of the pH at the deaerator inlet and the actual value of the pH at the deaerator inlet at time

[0084] In this embodiment, the parameters of the outer-loop PI controller and the inner-loop PI controller of the cascade PID algorithm are shown in Table 2 and can be adjusted according to the actual situation.

[0085] Table 2 Parameters of the outer-loop PI controller and the inner-loop PI controller of the cascade PID algorithm

[0086]

[0087] Among them, in the measurement link of the actual value of the pH at the economizer inlet and the actual value of the pH at the deaerator inlet, a first-order inertia link is added for filtering to suppress the random noise of the actual value of the pH at the economizer inlet and the actual value of the pH at the deaerator inlet. The formula of the first-order inertia link is expressed as:

[0088] ;

[0089] In the formula, is the Laplace operator; is the inertia time constant. In this embodiment is set to 2 - 5 to avoid poor filtering effect due to being too low, or due to If it is too high, it will cause excessive delay and can be adjusted according to the actual situation.

[0090] An electronic device, comprising a processor, a memory and a bus, wherein the processor and the memory are connected through the bus. Among them, the memory is used to store a set of program codes, and the processor is used to call the program codes stored in the memory to execute a chemical ammonia addition cascade control method based on an improved particle swarm optimization feedforward model.

[0091] A non-volatile computer storage medium stores computer-executable instructions that execute a chemical ammonia addition cascade control method based on an improved particle swarm optimization feedforward model.

[0092] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A chemical ammonia addition cascade control method based on an improved particle swarm optimization feedforward model is characterized in that: The steps include: Step S1: Establishing an improved particle swarm optimization feedforward model; By screening the variables related to the frequency of the ammonia pump during the operation of the thermal power unit, and considering the time lag relationship between the mechanism of the thermal power unit and the relevant variables, a feedforward model is constructed, and the improved particle swarm algorithm is used to identify the parameters of the feedforward model, and the improved particle swarm optimization feedforward model is obtained; The specific process of using the improved particle swarm algorithm to identify the parameters of the feedforward model is as follows: Step S1.21: Based on the feedforward model parameters , , , , , set the improved particle swarm algorithm The position of each particle is The fitness function of the improved particle swarm algorithm is the prediction error of the frequency of the ammonia pump The variance of is the water flow lag time, is the conductivity lag time of ammonia solution, is the pH lag time at the economizer inlet, and are the weight and bias of the economizer inlet pH value, Indicates time; Step S1.22: Initialize the improved particle swarm algorithm; determine the number of individual particles, the initial position and speed of each individual particle; Step S1.23: Calculate the fitness of each individual particle position according to the fitness function; Step S1.24: Update the extreme value of the particle individual position, that is, the historical optimal position of the particle individual: if the fitness of the current particle individual position is better than the global extreme value, that is, the optimal position of the current particle individual, then update the extreme value of the particle individual position ; Step S1.25: Update the global extreme value: If the fitness of the current particle individual position is better than the global extreme value, then update the global extreme value ; Step S1.26: Update individual particle speed: update the speed of each individual particle according to the individual particle position extreme value and the global extreme value; Step S1.27: updating the individual particle position according to the updated individual particle velocity; Step S1.28: Repeat the iteration steps S1.23 to S1.27 until the preset stop condition, i.e. the maximum number of iterations, is met; Step S1.29: After the preset stop condition is met, the individual particle position corresponding to the global extreme value is output as the optimal solution, i.e., the optimal feedforward model parameter; Step S2: inputting the feed water flow rate, the conductivity of the ammonia solution and the pH setting value at the economizer inlet into the improved particle swarm optimization feedforward model to generate a first predicted frequency of the ammonia pump; Step S3: inputting the difference between the actual pH value at the economizer inlet and the set pH value at the economizer inlet into the outer loop PI controller to generate the set pH value at the deaerator inlet, i.e., the set value of the inner loop PI controller; Step S4: inputting the difference between the actual pH value at the deaerator inlet and the set pH value at the deaerator inlet into the inner loop PI controller to generate a second predicted frequency of the ammonia addition pump; Step S5: Add the first predicted frequency of the ammonia adding pump and the second predicted frequency of the ammonia adding pump to obtain the total ammonia adding pump frequency, so as to realize automatic control of chemical ammonia addition.

2. The chemical ammonia addition cascade control method based on the improved particle swarm optimization feedforward model according to claim 1 is characterized in that: The feedforward model is expressed as: ； In the formula, for The frequency of ammonia pump at the moment; for Water flow rate at the moment; for Economizer inlet pH set value at the moment; for Conductivity of ammonia solution at time.

3. The chemical ammonia addition cascade control method based on the improved particle swarm optimization feedforward model according to claim 2 is characterized in that: In step S1.26, the update formula of the particle individual velocity is: ； ； In the formula, represents the inertia weight; and Respectively represent the maximum and minimum values ​​of the inertia weight; represents the base of natural logarithms; Indicates the current iteration number; Indicates the maximum number of iterations; represents the adjustment factor, which is 0.1; represents the Beta distribution; Both represent the shape parameters of the Beta distribution; Indicates The individual particles The speed of the moment; It is The individual particles The speed of the moment; and is the learning factor; and is a random number between [0,1]; express The individual position extreme value of the particle at the moment; express Global extremum at any moment.

4. The chemical ammonia addition cascade control method based on the improved particle swarm optimization feedforward model according to claim 3 is characterized in that: In step S1.27, the individual particle position is updated according to the updated individual particle velocity, which is expressed as: ； ； In the formula, Indicates The individual particles The location at the moment; Represents the dynamic adjustment factor.

5. The chemical ammonia addition cascade control method based on the improved particle swarm optimization feedforward model according to claim 4 is characterized in that: The specific process of step S3 is: input the difference between the actual pH value at the economizer inlet and the set pH value at the economizer inlet into the outer loop PI controller, and calculate the set pH value at the deaerator inlet through the proportional and integral controllers in the outer loop PI controller. , which is the setting value of the inner loop PI controller, is expressed as: ； In the formula, is the proportional gain coefficient of the outer loop PI controller; is the integral gain coefficient of the outer loop PI controller; for The difference between the set pH value at the economizer inlet and the actual pH value at the economizer inlet at the moment; for The difference between the set pH value at the economizer inlet and the actual pH value at the economizer inlet at a given moment.

6. The chemical ammonia addition cascade control method based on the improved particle swarm optimization feedforward model according to claim 5 is characterized in that: The specific process of step S4 is: input the difference between the actual pH value at the deaerator inlet and the set pH value at the deaerator inlet into the inner loop PI controller, and calculate the frequency of the second ammonia pump through the proportional and integral controllers in the inner loop PI controller. , expressed as: ； In the formula, is the proportional gain coefficient of the inner loop PI controller; is the integral gain coefficient of the inner loop PI controller; express The difference between the pH set value at the deaerator inlet and the actual pH value at the deaerator inlet at the moment; for The difference between the deaerator inlet pH set value and the deaerator inlet pH actual value at the moment.

7. The chemical ammonia addition cascade control method based on the improved particle swarm optimization feedforward model according to claim 6 is characterized in that: Calculate the deaerator inlet pH setpoint in the outer loop PI controller The inner loop PI controller calculates the frequency of the second ammonia pump When , a first-order inertia link is added for filtering.

8. An electronic device, characterized in that: The method comprises a processor, a memory and a bus, wherein the processor and the memory are connected via the bus, wherein the memory is used to store a set of program codes, and the processor is used to call the program codes stored in the memory to execute the chemical ammonia addition cascade control method based on the improved particle swarm optimization feedforward model as described in any one of claims 1 to 7.

9. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions execute the chemical ammonia addition cascade control method based on the improved particle swarm optimization feedforward model described in any one of claims 1-7.

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